The OP GitHub site doesn't promote any material that introduces the concepts at all.
The "survey" paper at the top is nigh-impenetrable. I'm sure the category theorists are having fun modelling machine learning, but it doesn't show how machine learning benefits from the category theory.
Category Theory (just as all mathematical models for programming or subsets thereof) are building blocks for reasoning on what we build. Past applications of such mathematical models include:
- programming languages with semantics that are better adapted to specific problems (e.g. Rust's ownership);
- better compilers (see e.g. Haskell's supercompiler, which puts to shame `constexpr`-style features);
- better static analyzers (e.g. better type systems, abstract interpretation, model checkers).
In the case of Machine Learning, it might some day help us create Machine Learning that we can understand and trust better. Or it might fail. Or it might help us invent something different entirely, in 30 years.
Intuition from category theory has certainly been useful for some programming language features (eg. algebraic effects, modal types or lenses), but I don't think it has helped with the things you cite. Rusts ownership model was created with little formal, academic understanding - or do you mean more foundational stuff like linear logic? And I don't think that Haskell does supercompilation today? It does optimize a whole lot but that is more due to laziness and other optimizations. But neither these optimizations nor supercompilation have much to do with category theory as far as I am aware.
Yeah, my phrasing is probably unclear. I meant to give examples of applications of formal semantics (huddling everything together). I should probably sleep instead of commenting on HN :)
But it is my understanding that Rust's ownership traces its roots back to both linear logic (well, affine logic) and region-based resource management, both of which have formal semantics.
I haven't followed what got merged into GHC, but I remember seeing demos (and a paper) of a Haskell supercompiler during a conference many years ago, so it is something.
In my sleep-deprived brain, supercompilation is directly related to algebraic effects (although probably not in Haskell itself), which are themselves related to category theory. I could be wrong.
These days, Microsoft requires model-checking proofs before accepting new device drivers. That's their secret weapon that finally got (mostly) rid of the BSOD.
I suspect that Apple is also using model-checking at various layers, but I have no proof :)
You're completely right that the repo doesn't promote any introductory material. CT is notoriously difficult to get into, and this repository wasn't meant to be a pedagogical one, but rather a list of all the relevant papers.
Though, I'll see about remedying this. I have a different repository that curates a list of (what I consider to be, as a CS major) relatively approachable CT introductory materials https://github.com/bgavran/Category_Theory_Resources
and it might be a good idea to add a pointer to it.
Olah's blog entry is about types and algebra.
We've had types and algebra for a long time distinct Category Theory, which is a further abstraction over algebraic structures.
A lot of computer programmers who never studied Abstract Algebra or Type Theory before think that all of that is Category Theory because Haskell is their first exposure to all that, and Category Theory is a big meme in Haskell, thanks to Monad.
The OP GitHub site doesn't promote any material that introduces the concepts at all. The "survey" paper at the top is nigh-impenetrable. I'm sure the category theorists are having fun modelling machine learning, but it doesn't show how machine learning benefits from the category theory.